Update app.py

app.py

@@ -16,7 +16,7 @@ from tqdm import tqdm
 
 
 st.title('A _Quickstart Notebook_ for :blue[ClimSim]:')
-st.link_button("ClimSim", "https://huggingface.co/datasets/LEAP/subsampled_low_res/tree/main",use_container_width=True,type="primary")
+st.link_button("ClimSim", "https://huggingface.co/datasets/LEAP/subsampled_low_res/tree/main",use_container_width=True)
 st.header('**Step 1:**  Import data_utils')
 st.code('''from data_utils import *''',language='python')
 st.header('**Step 2:**  Instantiate class')
@@ -24,7 +24,7 @@ st.header('**Step 3:**  Load training and validation data')
 
 st.header('**Step 4:**  Train models')
 st.subheader('Train constant prediction model')
-st.sidebar.link_button("Go to Original Dataset", "https://huggingface.co/datasets/LEAP/subsampled_low_res/tree/main")
+st.link_button("Go to Original Dataset", "https://huggingface.co/datasets/LEAP/subsampled_low_res/tree/main",,use_container_width=True)
 
 
 
@@ -121,12 +121,73 @@ fig.tight_layout()
 st.pyplot(fig)
 
 # path to target input
-data.input_scoring = np.load('score_input_smallnn.npy')
-
+data.input_scoring = np.load('score_input_small.npy')
 # path to target output
 data.target_scoring = np.load('scoring_target_small.npy')
+data.set_pressure_grid(data_split = 'scoring')
+# constant prediction
+const_pred_scoring = np.repeat(const_model[np.newaxis, :], data.target_scoring.shape[0], axis = 0)
+print(const_pred_scoring.shape)
+
+# multiple linear regression
+X_scoring = data.input_scoring
+bias_vector_scoring = np.ones((X_scoring.shape[0], 1))
+X_scoring = np.concatenate((X_scoring, bias_vector_scoring), axis=1)
+mlr_pred_scoring = X_scoring@mlr_weights
+print(mlr_pred_scoring.shape)
+
+# Your model prediction here
+
+# Load predictions into object
+data.model_names = ['const', 'mlr'] # model name here
+preds = [const_pred_scoring, mlr_pred_scoring] # add prediction here
+data.preds_scoring = dict(zip(data.model_names, preds))
+# weight predictions and target
+data.reweight_target(data_split = 'scoring')
+data.reweight_preds(data_split = 'scoring')
+
+# set and calculate metrics
+data.metrics_names = ['MAE', 'RMSE', 'R2', 'bias']
+data.create_metrics_df(data_split = 'scoring')
+
+# set plotting settings
+%config InlineBackend.figure_format = 'retina'
+letters = string.ascii_lowercase
+
+# create custom dictionary for plotting
+dict_var = data.metrics_var_scoring
+plot_df_byvar = {}
+for metric in data.metrics_names:
+    plot_df_byvar[metric] = pd.DataFrame([dict_var[model][metric] for model in data.model_names],
+                                               index=data.model_names)
+    plot_df_byvar[metric] = plot_df_byvar[metric].rename(columns = data.var_short_names).transpose()
 
+# plot figure
+fig, axes = plt.subplots(nrows  = len(data.metrics_names), sharex = True)
+for i in range(len(data.metrics_names)):
+    plot_df_byvar[data.metrics_names[i]].plot.bar(
+        legend = False,
+        ax = axes[i])
+    if data.metrics_names[i] != 'R2':
+        axes[i].set_ylabel('$W/m^2$')
+    else:
+        axes[i].set_ylim(0,1)
 
+    axes[i].set_title(f'({letters[i]}) {data.metrics_names[i]}')
+axes[i].set_xlabel('Output variable')
+axes[i].set_xticklabels(plot_df_byvar[data.metrics_names[i]].index, \
+    rotation=0, ha='center')
 
+axes[0].legend(columnspacing = .9,
+               labelspacing = .3,
+               handleheight = .07,
+               handlelength = 1.5,
+               handletextpad = .2,
+               borderpad = .2,
+               ncol = 3,
+               loc = 'upper right')
+fig.set_size_inches(7,8)
+fig.tight_layout()
+st.pyplot(fig)
 
 st.markdown('Streamlit p')